Shuffled complex evolution approach for effective and efficient global minimization
Journal of Optimization Theory and Applications
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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The genetic algorithm is a search procedure based on the mechanism of natural selection and natural genetics, which combines an artificial survival of the fittest with genetic operators extracted from nature. This paper introduces a real genetic algorithm (GA) for applying it to calibration of a conceptual rainfall-runoff model for real data from a catchment in the coastal area of Lattakia, Syria. All seven calibration parameters of the model have been optimized by minimizing the sum of squares of differences between computed and observed discharges. The GA was always able to find an objective function value close to the global minimum. In some optimization runs, the search landed at a local optimum, but this happened only when the objective function value of the local optimum was similar to that of the global optimum. A combination of a real GA and fine tuning using Sequential Simplex Method was applied to perform very effectively. The results proved that the real GA can be efficient and robust in the field of hydrology and in solving many different inverse problems and operation research problems in environmental modeling.